The present technology relates to a data processing apparatus, a data processing method, and a program, and particularly to a data processing apparatus, a data processing method, and a program, capable of easily and accurately obtaining power consumption or the like of, for example, each of a plurality of electrical appliances in households.
As a method of presenting power consumption or current consumption of, for example, each of electrical appliances such as household electrical appliances (electrical appliances for households) in a household or the like to a user in the household and realizing so-called “visualization” of power consumption or the like, there is a method of, for example, installing a smart tap in each outlet.
The smart tap has a measurement function of measuring power which is consumed by an outlet (to which a household electrical appliance is connected) in which the smart tap is installed and a communication function with an external device.
In the smart tap, power (consumption) measured with the measurement function is transmitted to a display or the like with the communication function, and, in the display, the power consumption from the smart tap is displayed, thereby realizing “visualization” of power consumption of each household electrical appliance.
However, installing smart taps in all the outlets in a household is not easy in terms of costs.
In addition, a household electrical appliance fixed in a house, such as a so-called built-in air conditioner may be directly connected to a power line without using outlets in some cases, and thus it is difficult to use the smart tap for such a household electrical appliance.
Therefore, a technique called NILM (Non-Intrusive Load Monitoring) in which, for example, in a household or the like, from information on current measured in a distribution board (power distribution board), power consumption or the like of each household electrical appliance in the household connected ahead thereof has attracted attention.
In the NILM, for example, using current measured in a location, power consumption of each household electrical appliance (load) connected ahead therefrom is obtained without individual measurement.
For example, PTL 1 discloses an NILM technique in which active power and reactive power are calculated from current and voltage measured in a location, and an electrical appliance is identified by clustering respective variation amounts.
In the technique disclosed in PTL 1, since variations in active power and reactive power when a household electrical appliance is turned on and off are used, variation points of the active power and reactive power are detected. For this reason, if detection of variation points fails, it is difficult to accurately identify the household electrical appliance.
Further, in recent household electrical appliances, it is difficult to represent operating states as two states of ON and OFF, and thus it is difficult to accurately identify household electrical appliances merely by using variations in active power and reactive power in an ON state and OFF state.
Therefore, for example, PTLs 2 and 3 disclose an NILM technique in which LMC (Large Margin Classifier) such as SVM (Support Vector Machine) is used as an identification model (discriminative model, Classification) of household electrical appliances.
However, in the NILM using the identification model, unlike a generative model such as an HMM (Hidden Markov Model), existing learning data is prepared for each household electrical appliance, and learning of an identification model using the learning data is required to be completed in advance.
For this reason, in the NILM using an identification model, it is difficult to handle a household electrical appliance where learning of an identification model is not performed using known learning data.
Therefore, for example, NPLs 1 and 2 disclose an NILM technique in which an HMM which is a generative model is used instead of an identification model of which learning is required using known learning data in advance.